Innella G, Erini G, De Leo A, Godino L, Caramanna L, Ferrari S, Miccoli S, Perrone A M, Zamagni C, De Iaco P, Turchetti D, Rucci P
Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy; Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
ESMO Open. 2025 Jun 27;10(7):105300. doi: 10.1016/j.esmoop.2025.105300.
Identification of germline BRCA1/2 pathogenic variants is crucial for tailoring ovarian cancer treatment and prevention. The purpose of the study was to develop a model to predict BRCA1/2 status in ovarian cancer patients.
The association between clinical-pathological features and BRCA1/2 status was analysed in a series of 1009 ovarian cancer patients, using Fisher's exact test. Logistic regression models and a decision tree classification algorithm were used to develop a risk score.
Compared with noncarriers, BRCA1/2 carriers (n = 216; 21.4%) presented more frequently with serous histotype non-low-grade (92.3% versus 71.6%, P < 0.001), family history of ovarian cancer (31.6% versus 5.7%, P < 0.001), family history of breast cancer (53.7% versus 31.6%, P < 0.001), previous breast cancer (20.9% versus 8.5%, P < 0.001), advanced stage (78.8% versus 69.5%, P = 0.019) and younger age (56.9 years versus 60.8 years, P < 0.001). Multivariable logistic regression on 648 patients with complete data confirmed histotype, family history of breast/ovarian cancer, previous breast cancer and age as independently and significantly associated with BRCA1/2 status. After refining the categorization of variables according to decision tree classification algorithm results, odds ratios derived from multivariable logistic regression were used to assign weights from 1 to 3 to each feature (non-low-grade serous histotype = 3, low-grade serous/high-grade endometrioid histotype/family history of ovarian cancer = 2, age at diagnosis <50 years/family and personal history of breast cancer = 1) and to develop a score ranging from 0 to 10, associated with increasing risk of BRCA1/2 variants (from 0.6% for score 0 to 88% for score ≥7). The area under the curve of the score was 0.78 (95% confidence interval 0.74-0.82) and the optimal cut-off was ≥4 points with a sensitivity of 81% and a specificity of 62.3%.
The proposed risk score may improve assessment and counselling of ovarian cancer patients.
识别种系BRCA1/2致病变异对于定制卵巢癌的治疗和预防方案至关重要。本研究的目的是开发一种模型来预测卵巢癌患者的BRCA1/2状态。
采用Fisher精确检验分析了1009例卵巢癌患者的临床病理特征与BRCA1/2状态之间的关联。使用逻辑回归模型和决策树分类算法来制定风险评分。
与非携带者相比，BRCA1/2携带者（n = 216；21.4%）更常表现为浆液性组织学类型非低级别（92.3%对71.6%，P < 0.001）、有卵巢癌家族史（31.6%对5.7%，P < 0.001）、有乳腺癌家族史（53.7%对31.6%，P < 0.001）、既往有乳腺癌（20.9%对8.5%，P < 0.001）、晚期（78.8%对69.5%，P = 0.019）以及年龄较小（56.9岁对60.8岁，P < 0.001）。对648例有完整数据的患者进行多变量逻辑回归分析，证实组织学类型、乳腺/卵巢癌家族史、既往乳腺癌和年龄与BRCA1/2状态独立且显著相关。根据决策树分类算法结果对变量分类进行细化后，将多变量逻辑回归得出的数据用于为每个特征赋予1至3的权重（非低级别浆液性组织学类型 = 3，低级别浆液性/高级别子宫内膜样组织学类型/卵巢癌家族史 = 2，诊断时年龄<50岁/乳腺癌家族史和个人史 = 1），并制定一个范围从0到10的评分，该评分与BRCA1/2变异风险增加相关（评分0时为0.6%，评分≥7时为88%）。该评分的曲线下面积为0.78（95%置信区间0.74 - 0.82），最佳截断值为≥4分，敏感性为81%，特异性为62.3%。
所提出的风险评分可能会改善对卵巢癌患者的评估和咨询。
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